Learn how to enhance AI query capabilities and data accuracy through the application of LlamaIndex in retrieval-augmented generation processes.
Overview
Syllabus
Introduction
- Overcome the limitations of LLMs with RAG
- Limitations of LLMs
- Use cases for retrieval-augmented generation (RAG)
- Using GitHub Codespaces
- Setting up your environment
- Choosing an LLM and embeddings provider
- Setting up LLM accounts
- Choosing a vector database
- Setting up a Qdrant account
- Downloading our data
- How LlamaIndex is organized
- Using LLMs
- Loading data
- Indexing
- Storing and retrieving
- Querying
- Agents
- Components of a RAG system
- Ingestion pipeline
- Query pipeline
- Prompt engineering for RAG
- Data preparation for RAG
- Putting it all together
- Drawbacks of Naive RAG
- Introduction to RAG evaluation
- Evaluation metrics
- How to create an evaluation set
- How we can improve on Naive RAG
- Optimizing chunk size
- Small to big retrieval
- Semantic chunking
- Metadata extraction
- Document summary index
- Query transformation
- Node post-processing
- Re-ranking
- FLARE
- Prompt compression
- Self-correcting
- Hybrid retrieval
- Agentic RAG
- Ensemble retrieval
- Ensemble query engine
- LlamaIndex evaluation
- Comparative analysis of retrieval-augmented generation techniques
Taught by
Harpreet Sahota